python - OneHotEncoder 对象没有属性 get_feature_names_out

Question

我试图通过此链接了解 OneHotEncoder 和 get_dummies 之间的差异:enter link description here

当我编写完全相同的代码时，我收到一个错误，它显示

AttributeError: 'OneHotEncoder' object has no attribute 'get_feature_names_out'

这是代码:

import pandas as pd
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

df = sns.load_dataset('tips')
df = df[['total_bill', 'tip', 'day', 'size']]

df.head(5)

X = df.drop('tip', axis=1)
y = df['tip']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

ohe = OneHotEncoder(handle_unknown='ignore', sparse=False, dtype='int')
ohe.fit(X_train[['day']])

def get_ohe(df):
    temp_df = pd.DataFrame(data=ohe.transform(df[['day']]), columns=ohe.get_feature_names_out())
    df.drop(columns=['day'], axis=1, inplace=True)
    df = pd.concat([df.reset_index(drop=True), temp_df], axis=1)
    return df

X_train = get_ohe(X_train)
X_test = get_ohe(X_test)

X_train.head()

我从 sklearn.preprocessing 模块检查了 OneHotEncoder，并且 get_feature_names_out() 方法存在并且并未弃用。我不知道为什么会收到此错误。

Answer 1

如果您使用的 scikit-learn 版本低于 1.0，则需要使用 get_feature_names方法。对于较新版本的 scikit-learn，get_feature_names_out会工作得很好。